Timely diagnosis of plant diseases is vital for minimizing crop loss and improving productivity, especially for smallholder farmers in resource-constrained regions. This work introduces FarmaFriend, an end-to-end mobile-ready solution that combines an enhanced YOLOv8x-based object detection model with a LoRA-tuned Small Language Model (SLM) for real-time plant disease identification and contextual report generation. The vision module, FarmaYOLOx, integrates Normalized Attention Module, Scale-Transfer Attention, and DySnakeConv, a dynamic deformable convolution module for robust lesion detection, achieving 78.6% precision and 66.2% mAP@0.5. The Phi-2 SLM generated structured reports with a BERT Score of 0.89. A React-based UI enables seamless user interaction, even in offline environments. Unlike isolated visual or language models, FarmaFriend bridges both modalities, empowering farmers with accessible, interpretable disease diagnostics. Future work will focus on drone-based deployment, expanding disease classes, and integrating weather and soil data to deliver hyperlocal crop care solutions on scale.